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Likelihood‐based Inference with Missing Data Under Missing‐at‐Random
Scandinavian Journal of Statistics, 2015AbstractLikelihood‐based inference with missing data is challenging because the observed log likelihood is often an (intractable) integration over the missing data distribution, which also depends on the unknown parameter. Approximating the integral by Monte Carlo sampling does not necessarily lead to a valid likelihood over the entire parameter space ...
Yang, Shu, Kim, Jae Kwang
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Imputation Methods Outperform Missing-Indicator for Data Missing Completely at Random
2019 International Conference on Data Mining Workshops (ICDMW), 2019Missing data is a ubiquitous cross-domain problem persistent in the context of big data analytics. Approaches to deal with missing data can be partitioned into methods that impute substitute values and methods that introduce missing-indicator variables.
António Pereira Barata +3 more
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On the Distribution of the Number of Missing Words in Random Texts
Combinatorics, Probability and Computing, 2003Determining the distribution of the number of empty urns after a number of balls have been thrown randomly into the urns is a classical and well understood problem. We study a generalization: Given a finite alphabet of size σ and a word length q, what is the distribution of the number X of words (of length q) that do not occur in a random text of ...
Sven Rahmann, Eric Rivals
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Analysis of missing data with random forests
2012Random Forests are widely used for data prediction and interpretation purposes. They show many appealing characteristics, such as the ability to deal with high dimensional data, complex interactions and correlations. Furthermore, missing values can easily be processed by the built-in procedure of surrogate splits.
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Missing Inaction: Preventing Missing Outcome Data in Randomized Clinical Trials
Journal of Biopharmaceutical Statistics, 2009Many methods are available to deal with missing data in randomized clinical trials, and active statistical research in the area continues. When, however, a high proportion of outcome data is missing, the methods can produce inaccurate estimates of the true effect size.
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Score Test for Missing at Random or Not under Logistic Missingness Models
Biometrics, 2023Zhiping Lu, Yukun Liu
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Semiparametric Inference for Nonmonotone Missing-Not-at-Random Data: The No Self-Censoring Model
Journal of the American Statistical Association, 2022Daniel Malinsky +2 more
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